Experimental Setup:
To extract the features from Twitter and generate the dataset we used Python 3.5. The python script was executed locally on a machine with the following configuration:
Intel Core i7, 2.80*8 GHZ, 32GB, Ubuntu 16.04 LTS 64 bit.
For the training and evaluation of the machine learning models, we switched to Google Colab. We use the modAL framework [1]}, which is an active learning framework for python. It is a flexible, modular, and extensible framework built on top of Scikit learn. For the learner to query the instance labels, we use pool-based sampling and for the query strategy, we use different sampling techniques. For classification purposes, we use two RFC and SVC classifiers implemented using the scikit-learn library.
